1. Field
The present disclosure relates generally to processing images and, in particular, to reducing clutter in images. Still more particularly, the present disclosure relates to a method and apparatus for reducing clutter that changes with respect to space and time in a number of images.
2. Background
Sensor systems are commonly used to detect target objects. As used herein, a “target object” is an object of interest. The object of interest may be, for example, without limitation, an aircraft, an unmanned aerial vehicle (UAV), a projectile, a watercraft, a spacecraft, a vehicle, a weather phenomenon, a living organism, or some other suitable type of object.
Typically, the sensor system used to detect a target object is configured to detect electromagnetic radiation and convert this electromagnetic radiation into electrical signals that can be used to form images. These images may be, for example, without limitation, radar images, infrared images, ultraviolet images, visible light images, or other suitable types of images.
In some situations, a portion of the electromagnetic radiation detected by the imaging system is undesired electromagnetic radiation. Some or all of this undesired electromagnetic radiation may be clutter. As used herein, “clutter” describes electromagnetic radiation reflecting off of and/or emanating from sources that are not target objects. These sources may include, for example, without limitation, the ground, the surface of a body of water, precipitation, radar countermeasures, atmospheric turbulences, weather conditions, trees, foliage, waves, animals, birds, insects, and other types of sources other than a target object.
The presence of clutter in an image may make detecting a target object in the image more difficult than desired. Some currently available solutions for reducing the clutter in an image use spatial filters, spatial-temporal filters, and/or spatial-spectral filters. These different types of filters may use fixed weights to characterize the clutter in an image. However, with fixed weights, these types of filters may be unable to characterize the variations of clutter in different locations in an image with a desired level of accuracy. Consequently, the solutions using these types of filters may be unable to provide a desired level of reduction in clutter throughout the entire image.
Further, when used to reduce clutter in a sequence of images generated over time, filters that use fixed weights to characterize clutter may not take into account changes in the clutter over time. Clutter that changes over time may be referred to as non-stationary clutter. Some currently available solutions for reducing clutter that use filters with fixed weights may be unable to provide the desired level of reduction in clutter in all of the images in a sequence of images. Therefore, it would be desirable to have a method and apparatus that takes into account at least some of the issues discussed above, as well as other possible issues.